Generally, in a duplex printed sheet it is desirable and expected that images on each side of a page are aligned correctly on a page. A common test is to print a test pattern on each side of a sheet such that with perfect alignment the registration marks will be aligned when the sheet is viewed is a see-thru fashion (typically, holding the sheet to a light source so that a customer can visually test alignment). There are numerous reasons why registration may be poor, for example including but not limited to image distortions or placement on sheet due to situations such as paper shrinkage due to the fusing step, mechanical misalignment of the sheet in the paper path, or magnification errors caused by the xerographic process. Further, in situations where the sheet is fused twice and inverted for duplex, alignment and printable substrate shrinkage typically varies on a per sheet side basis. As such undesired registration errors are introduced by the inherent limitations and anomalies of the device. Registration of image-to-image, image-to-sheet, or sheet-to-device can be suboptimal for multiple reasons, including paper shrinkage and setup error caused by the operator or by device limitations. Poor show-through or see-through alignment and/or poor highlight color (HLC) registration results in reduced Customer satisfaction.
Systems for control of image placement provided by conventional measurements are typically limited. For example, in most conventional systems, only printer spatial errors at the corners of a sheet are determined. For most media, the measurement process is typically performed manually. Errors that occur during printing or scanning can be caused by, for example, Raster Output Scanner (ROS) scan line bow, ROS magnification, lateral errors, and skew errors, which cannot be determined from measurements taken using conventional techniques. Paper shrinkage caused by the fusing process can also result in errors that have well defined intra sheet signatures.
Existing registration methods typically use basic algebra/trigonometry and piece-wise mechanical and xerographic setup adjustments. For example, calculating rotation needed after a shift. Alignment in imaging contexts is also common. Further, other 2D least squares techniques ordinarily use a complicated polynomial form (e.g., y=β0+β1x+β2x2+β3x3) or complex multivariate form (e.g., y=β0+β1u+β2v+β3u2+β4uv+β5v2) which is incompatible or less well suited for commonly available image processing systems/tools. See: Linear Algebra and its Applications, by David C. Lay, Addison Wesley, 3rd Ed., (Jul. 18, 2002). ISBN-10: 0201709708, ISBN-13: 978-0201709704, the teachings of which are incorporated herein by reference in its entirety.
Accordingly, what is needed in this art are systems and methods for correcting image-to-image, image-to-sheet and sheet-to-device registration errors which overcome many of the above-identified problems with existing registration techniques.